COVID-19 in Ethiopia: A contextual approach to explaining its slow growth
Bibliographic record
Abstract
T he COVID-19 pandemic is a time of many predictions and rapid learning. Early on, the World Health Organization (WHO) identified Ethiopia as one of thirteen countries in Africa as a top priority for COVID-19 preparedness Surprisingly and thankfully, while an early catastrophic outbreak was feared, the rise in COVID-19 cases in Ethiopia has continued to be slow, with few significant health systems impacts or changes in the patterns of morbidity and mortality [2]. Despite its high-risk status, over four months after is first confirmed case, Ethiopia, with a population of 110 million, reported only 8181 cases of COVID-19 on July 15th, 2020, or 74.37 cases per 1 million people [2]. In comparison, the United States, with a population three times that of Ethiopia, had over 2 million cases within four months of its first case Brazil, with a population twice the size of Ethiopia, had over 1 million cases within four months of its first case, and South Africa, with a population half that of Ethiopia, had over 150 000 cases within four months of its first case
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".